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未验证 提交 fe0ab9c8 编写于 作者: T TeslaZhao 提交者： GitHub
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Merge pull request #1289 from HexToString/develop-p 

C++ Serving更新
上级 82be95f0 4faeb510
隐藏空白更改
内联 并排
Showing with 1019 addition and 788 deletion
CMakeLists.txt
浏览文件 @ fe0ab9c8
......@@ -30,7 +30,7 @@ find_package(Threads REQUIRED)
find_package(CUDA QUIET)
include(simd)
# SET(CMAKE_BUILD_TYPE "Debug")
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING
......
README.md
浏览文件 @ fe0ab9c8
......@@ -175,9 +175,12 @@ python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --p
| Argument | Type | Default | Description |
| ---------------------------------------------- | ---- | ------- | ----------------------------------------------------- |
| `thread` | int | `4` | Concurrency of current service |
| `thread` | int | `2` | Number of brpc service thread |
| `op_num` | int[]| `0` | Thread Number for each model in asynchronous mode |
| `op_max_batch` | int[]| `0` | Batch Number for each model in asynchronous mode |
| `gpu_ids` | str[]| `"-1"` | Gpu card id for each model |
| `port` | int | `9292` | Exposed port of current service to users |
| `model` | str | `""` | Path of paddle model directory to be served |
| `model` | str[]| `""` | Path of paddle model directory to be served |
| `mem_optim_off` | - | - | Disable memory / graphic memory optimization |
| `ir_optim` | bool | False | Enable analysis and optimization of calculation graph |
| `use_mkl` (Only for cpu version) | - | - | Run inference with MKL |
......@@ -186,7 +189,24 @@ python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --p
| `use_xpu` | - | - | Run PaddleLite inference with Baidu Kunlun XPU |
| `precision` | str | FP32 | Precision Mode, support FP32, FP16, INT8 |
| `use_calib` | bool | False | Only for deployment with TensorRT |
| `gpu_multi_stream` | bool | False | EnableGpuMultiStream to get larger QPS |
#### Description of asynchronous model
Asynchronous mode is suitable for 1. When the number of requests is very large, 2. When multiple models are concatenated and you want to specify the concurrency number of each model.
Asynchronous mode helps to improve the throughput (QPS) of service, but for a single request, the delay will increase slightly.
In asynchronous mode, each model will start n threads of the number you specify, and each thread contains a model instance. In other words, each model is equivalent to a thread pool containing N threads, and the task is taken from the task queue of the thread pool to execute.
In asynchronous mode, each RPC server thread is only responsible for putting the request into the task queue of the model thread pool. After the task is executed, the completed task is removed from the task queue.
In the above table, the number of RPC server threads is specified by --thread, and the default value is 2.
--op_num specifies the number of threads in the thread pool of each model. The default value is 0, indicating that asynchronous mode is not used.
--op_max_batch specifies the number of batches for each model. The default value is 32. It takes effect when --op_num is not 0.
#### When you want a model to use multiple GPU cards.
python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --gpu_ids 0,1,2
#### When you want 2 models.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292
#### When you want 2 models, and want each of them use multiple GPU cards.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292 --gpu_ids 0,1 1,2
#### When a service contains two models, and each model needs to specify multiple GPU cards, and needs asynchronous mode, each model specifies different concurrency number.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292 --gpu_ids 0,1 1,2 --op_num 4 8
</center>
```python
......
README_CN.md
浏览文件 @ fe0ab9c8
......@@ -172,19 +172,40 @@ python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --p
```
<center>
| Argument | Type | Default | Description |
| ---------------------------------------------- | ---- | ------- | ------------------------------------------------------ |
| `thread` | int | `4` | Concurrency of current service |
| `port` | int | `9292` | Exposed port of current service to users |
| `name` | str | `""` | Service name, can be used to generate HTTP request url |
| `model` | str | `""` | Path of paddle model directory to be served |
| `mem_optim_off` | - | - | Disable memory optimization |
| `ir_optim` | bool | False | Enable analysis and optimization of calculation graph |
| `use_mkl` (Only for cpu version) | - | - | Run inference with MKL |
| `use_trt` (Only for Cuda>=10.1 version) | - | - | Run inference with TensorRT |
| `use_lite` (Only for Intel x86 CPU or ARM CPU) | - | - | Run PaddleLite inference |
| `use_xpu` | - | - | Run PaddleLite inference with Baidu Kunlun XPU |
| `precision` | str | FP32 | Precision Mode, support FP32, FP16, INT8 |
| Argument | Type | Default | Description |
| ---------------------------------------------- | ---- | ------- | ----------------------------------------------------- |
| `thread` | int | `2` | Number of brpc service thread |
| `op_num` | int[]| `0` | Thread Number for each model in asynchronous mode |
| `op_max_batch` | int[]| `32` | Batch Number for each model in asynchronous mode |
| `gpu_ids` | str[]| `"-1"` | Gpu card id for each model |
| `port` | int | `9292` | Exposed port of current service to users |
| `model` | str[]| `""` | Path of paddle model directory to be served |
| `mem_optim_off` | - | - | Disable memory / graphic memory optimization |
| `ir_optim` | bool | False | Enable analysis and optimization of calculation graph |
| `use_mkl` (Only for cpu version) | - | - | Run inference with MKL |
| `use_trt` (Only for trt version) | - | - | Run inference with TensorRT |
| `use_lite` (Only for Intel x86 CPU or ARM CPU) | - | - | Run PaddleLite inference |
| `use_xpu` | - | - | Run PaddleLite inference with Baidu Kunlun XPU |
| `precision` | str | FP32 | Precision Mode, support FP32, FP16, INT8 |
| `use_calib` | bool | False | Only for deployment with TensorRT |
| `gpu_multi_stream` | bool | False | EnableGpuMultiStream to get larger QPS |
#### 异步模型的说明
异步模式适用于1、请求数量非常大的情况，2、多模型串联，想要分别指定每个模型的并发数的情况。
异步模式有助于提高Service服务的吞吐（QPS），但对于单次请求而言，时延会有少量增加。
异步模式中，每个模型会启动您指定个数的N个线程，每个线程中包含一个模型实例，换句话说每个模型相当于包含N个线程的线程池，从线程池的任务队列中取任务来执行。
异步模式中，各个RPC Server的线程只负责将Request请求放入模型线程池的任务队列中，等任务被执行完毕后，再从任务队列中取出已完成的任务。
上表中通过 --thread 10 指定的是RPC Server的线程数量，默认值为2，--op_num 指定的是各个模型的线程池中线程数N，默认值为0，表示不使用异步模式。
--op_max_batch 指定的各个模型的batch数量，默认值为32，该参数只有当--op_num不为0时才生效。
#### 当您的某个模型想使用多张GPU卡部署时.
python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --gpu_ids 0,1,2
#### 当您的一个服务包含两个模型部署时.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292
#### 当您的一个服务包含两个模型，且每个模型都需要指定多张GPU卡部署时.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292 --gpu_ids 0,1 1,2
#### 当您的一个服务包含两个模型，且每个模型都需要指定多张GPU卡，且需要异步模式每个模型指定不同的并发数时.
python3 -m paddle_serving_server.serve --model uci_housing_model_1 uci_housing_model_2 --thread 10 --port 9292 --gpu_ids 0,1 1,2 --op_num 4 8
</center>
......
core/configure/proto/server_configure.proto
浏览文件 @ fe0ab9c8
......@@ -21,11 +21,12 @@ message EngineDesc {
required string reloadable_meta = 3;
required string reloadable_type = 4;
required string model_dir = 5;
required int32 runtime_thread_num = 6;
required int32 batch_infer_size = 7;
required int32 enable_batch_align = 8;
optional string version_file = 9;
optional string version_type = 10;
repeated int32 gpu_ids = 6;
required int32 runtime_thread_num = 7;
required int32 batch_infer_size = 8;
required int32 enable_batch_align = 9;
optional string version_file = 10;
optional string version_type = 11;
/*
* Sparse Parameter Service type. Valid types are:
......@@ -38,16 +39,17 @@ message EngineDesc {
LOCAL = 1;
REMOTE = 2;
}
optional SparseParamServiceType sparse_param_service_type = 11;
optional string sparse_param_service_table_name = 12;
optional bool enable_memory_optimization = 13;
optional bool enable_ir_optimization = 14;
optional bool use_trt = 15;
optional bool use_lite = 16;
optional bool use_xpu = 17;
optional bool use_gpu = 18;
optional bool combined_model = 19;
optional bool encrypted_model = 20;
optional SparseParamServiceType sparse_param_service_type = 12;
optional string sparse_param_service_table_name = 13;
optional bool enable_memory_optimization = 14;
optional bool enable_ir_optimization = 15;
optional bool use_trt = 16;
optional bool use_lite = 17;
optional bool use_xpu = 18;
optional bool use_gpu = 19;
optional bool combined_model = 20;
optional bool encrypted_model = 21;
optional bool gpu_multi_stream = 22;
};
// model_toolkit conf
......
core/general-client/src/general_model.cpp 100644 → 100755
浏览文件 @ fe0ab9c8
......@@ -166,6 +166,8 @@ int PredictorClient::numpy_predict(
batch_size = batch_size > string_feed_batch.size() ? batch_size
: string_feed_batch.size();
VLOG(2) << "batch size: " << batch_size;
// batch_size must be 1, cause batch is already in Tensor.
// I suggest to remove the outside vector<>.
predict_res_batch.clear();
Timer timeline;
int64_t preprocess_start = timeline.TimeStampUS();
......@@ -188,6 +190,8 @@ int PredictorClient::numpy_predict(
}
int vec_idx = 0;
// batch_size can only be 1, cause batch is already in Tensor.
// if batch_size is not 1, error will occur in C++ part.
for (int bi = 0; bi < batch_size; bi++) {
VLOG(2) << "prepare batch " << bi;
std::vector<Tensor *> tensor_vec;
......
core/general-server/op/general_reader_op.cpp
浏览文件 @ fe0ab9c8
......@@ -93,6 +93,9 @@ int GeneralReaderOp::inference() {
res->SetLogId(log_id);
Timer timeline;
int64_t start = timeline.TimeStampUS();
// only get insts(0), cause batch is already in Tensor.
// req can only include 1 inst.
// var_num means the number of feed_var.
int var_num = req->insts(0).tensor_array_size();
VLOG(2) << "(logid=" << log_id << ") var num: " << var_num
......@@ -178,7 +181,10 @@ int GeneralReaderOp::inference() {
VLOG(2) << "(logid=" << log_id << ") tensor size for var[" << i
<< "]: " << data_len;
databuf_size = data_len * elem_size;
out->at(i).data.Resize(databuf_size);
void *databuf_char = MempoolWrapper::instance().malloc(databuf_size);
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
out->at(i).data = paddleBuf;
// out->at(i).data.Resize(databuf_size);
if (out->at(i).lod.size() > 0) {
VLOG(2) << "(logid=" << log_id << ") var[" << i
<< "] has lod_tensor and len=" << out->at(i).lod[0].back();
......
core/predictor/framework/bsf-inl-tensor.h 已删除 100644 → 0
浏览文件 @ 82be95f0
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#ifdef BCLOUD
#include <base/atomicops.h>
#else
#include <butil/atomicops.h>
#endif
#include <errno.h>
#include <algorithm>
#include <deque>
#include <vector>
#include "core/predictor/common/inner_common.h"
#include "core/predictor/framework/infer_data.h"
#include "core/predictor/framework/memory.h"
#include <boost/function.hpp>
namespace im {
namespace bsf {
template <>
struct Task<baidu::paddle_serving::predictor::Tensor,
baidu::paddle_serving::predictor::Tensor> {
typedef Task<baidu::paddle_serving::predictor::Tensor,
baidu::paddle_serving::predictor::Tensor>
TaskT;
typedef baidu::paddle_serving::predictor::Tensor Tensor;
typedef baidu::paddle_serving::predictor::Tensor InType;
typedef baidu::paddle_serving::predictor::Tensor OutType;
typedef baidu::paddle_serving::predictor::BatchTensor BatchTensor;
typedef baidu::paddle_serving::predictor::BatchTensor InArrayT;
typedef baidu::paddle_serving::predictor::BatchTensor OutArrayT;
struct Segment {
Segment(void* p, size_t b, size_t s) : ptr(p), begin(b), size(s) {}
void* ptr;
size_t begin;
size_t size;
};
int read_fd;
int write_fd;
pid_t owner_tid;
const InArrayT* in;
OutArrayT* out;
size_t rem;
size_t size;
butil::atomic<size_t> index;
const BatchTensor* get(bool is_in) const {
if (is_in) {
return in;
} else {
return out;
}
}
BatchTensor* get(bool is_in) {
if (is_in) {
return const_cast<BatchTensor*>(in);
} else {
return out;
}
}
Task() {
read_fd = -1;
write_fd = -1;
owner_tid = -1;
in = NULL;
out = NULL;
rem = -1;
size = -1;
index.store(0, butil::memory_order_relaxed);
}
};
template <>
class BatchTasks<Task<baidu::paddle_serving::predictor::Tensor,
baidu::paddle_serving::predictor::Tensor>> {
public:
typedef baidu::paddle_serving::predictor::Tensor Tensor;
typedef baidu::paddle_serving::predictor::Tensor InType;
typedef baidu::paddle_serving::predictor::Tensor OutType;
typedef baidu::paddle_serving::predictor::DataBuf DataBuf;
typedef baidu::paddle_serving::predictor::MempoolWrapper MempoolWrapper;
typedef Task<baidu::paddle_serving::predictor::Tensor,
baidu::paddle_serving::predictor::Tensor>
TaskT;
typedef TaskMeta<TaskT> TaskMetaT;
typedef TaskT::InArrayT InArrayT;
typedef TaskT::OutArrayT OutArrayT;
explicit BatchTasks(size_t batch_size, bool batch_align = false)
: _batch_size(batch_size),
_rem_size(batch_size),
_batch_align(batch_align) {
_batch_in.clear();
_batch_out.clear();
_tasks.clear();
}
~BatchTasks() {
_batch_in.clear();
_batch_out.clear();
_tasks.clear();
}
static bool check_valid(const InArrayT& in,
OutArrayT& out, // NOLINT
bool align) { // NOLINT
if (align) {
if (out.count() <= 0 || out.size() <= 0) {
LOG(ERROR) << "Out tensor is empty, when aligned";
return false;
}
if (out.size() != in.size()) {
LOG(ERROR) << "In/Out tensor size not eq: " << out.size()
<< "!=" << in.size();
return false;
}
for (size_t fi = 0, shape0 = 0; fi < out.count(); ++fi) {
if (!out[fi].valid()) {
LOG(ERROR) << "Out[" << fi << "] tensor not valid";
return false;
}
if (out.size() != out[fi].shape0()) {
LOG(ERROR) << "Shape0 not consistency, " << out.size()
<< "!=" << out[fi].shape0() << ", " << fi;
return false;
}
}
}
return true;
}
size_t append_task(TaskT* task) {
size_t add = std::min(task->rem, _rem_size);
if (!_batch_align) {
add = task->rem;
}
TaskMetaT tm(task, task->in->size() - task->rem, add);
_tasks.push_back(tm);
task->rem -= add;
_rem_size -= add;
return _rem_size;
}
void merge_tasks() {
merge_input();
merge_output();
}
void merge_input() {
if (_tasks.size() <= 0 || _tasks[0].task->in->count() <= 0) {
return;
}
if (_tasks.size() == 1 && !_batch_align) {
TaskMetaT& tm = _tasks[0];
_batch_in = *(tm.task->in);
return;
}
merge_tensor(true);
}
void merge_output() {
if (_batch_align) {
if (_tasks.size() <= 0 || _tasks[0].task->out->count() <= 0) {
return;
}
}
if (_tasks.size() <= 0 || _tasks[0].task->out->count() <= 0) {
return;
}
TaskMetaT& tm = _tasks[0];
if (_tasks.size() == 1 && !_batch_align) {
_batch_out = *(tm.task->out);
return;
}
if (tm.task->out->size() <= 0) {
// shape is empty
_batch_out = *(tm.task->out);
return;
}
if ((*tm.task->out)[0].data.data() == 0 ||
(*tm.task->out)[0].data.size() == 0) {
_batch_out = *(tm.task->out);
return;
}
merge_tensor(false);
}
void merge_tensor(bool is_in) {
// accumulate batch size from fetched tasks
size_t batch_size = 0;
for (size_t ti = 0; ti < _tasks.size(); ++ti) {
TaskMetaT& tm = _tasks[ti];
size_t add = tm.end - tm.begin;
batch_size += add;
}
// merge all instanses in each tensor data
size_t tensor_count = _tasks[0].task->get(is_in)->count();
for (size_t fi = 0; fi < tensor_count; ++fi) {
const Tensor& head = (*(_tasks[0].task->get(is_in)))[fi];
Tensor batch_tensor;
batch_tensor.name = head.name;
batch_tensor.type = head.type;
batch_tensor.shape.push_back(batch_size);
size_t ins_ele_count = 1;
for (size_t si = 1; si < head.shape.size(); ++si) {
batch_tensor.shape.push_back(head.shape[si]);
ins_ele_count *= head.shape[si];
}
size_t tensor_ele_count = ins_ele_count * batch_size;
size_t ins_byte = ins_ele_count * head.ele_byte();
size_t tensor_byte = tensor_ele_count * head.ele_byte();
void* data_buf = MempoolWrapper::instance().malloc(tensor_byte);
if (!data_buf) {
LOG(ERROR) << "Malloc failed, size: " << tensor_byte;
return;
}
size_t data_byte = 0;
for (size_t ti = 0; ti < _tasks.size(); ++ti) {
TaskMetaT& tm = _tasks[ti];
size_t acc_byte = ins_byte * (tm.end - tm.begin);
if (data_byte + acc_byte > tensor_byte) {
LOG(ERROR) << "Invalid bytes: " << data_byte << " + " << acc_byte
<< " >= " << tensor_byte;
return;
}
const Tensor& tensor = (*(tm.task->get(is_in)))[fi];
memcpy(
reinterpret_cast<char*>(data_buf) + data_byte,
reinterpret_cast<char*>(tensor.data.data()) + tm.begin * ins_byte,
acc_byte);
data_byte += acc_byte;
}
if (data_byte != tensor_byte) {
LOG(ERROR) << "Invalid tensor byte: " << data_byte
<< " != " << tensor_byte;
return;
}
batch_tensor.data =
DataBuf(reinterpret_cast<char*>(data_buf), tensor_byte);
if (is_in) {
_batch_in.push_back(batch_tensor);
} else {
_batch_out.push_back(batch_tensor);
}
}
LOG(INFO) << "merge input(" << is_in << ") samples: " << batch_size
<< " from " << _tasks.size() << " pvs";
}
void notify_tasks() {
if (_batch_out.size() != _batch_in.size()) {
LOG(ERROR) << "batch size not consistency: " << _batch_out.size()
<< " != " << _batch_in.size();
return;
}
size_t tensor_count = _batch_out.count();
size_t batch_size = _batch_out.size();
for (size_t fi = 0; fi < tensor_count; ++fi) {
const Tensor& tensor = _batch_out[fi];
size_t ins_byte = tensor.ele_byte();
for (size_t si = 1; si < tensor.shape.size(); ++si) {
ins_byte *= tensor.shape[si];
}
for (size_t ti = 0, bi = 0, add = 0; ti < _tasks.size();
++ti, bi += add) {
OutArrayT* dst = _tasks[ti].task->out;
add = _tasks[ti].end - _tasks[ti].begin;
size_t offset_src = ins_byte * bi;
size_t add_byte = add * ins_byte;
if (_batch_align) { // merge all batchs
size_t offset_dst = ins_byte * _tasks[ti].begin;
void* ptr = const_cast<void*>((*dst)[fi].data.data());
memcpy(
reinterpret_cast<char*>(ptr) + offset_dst,
reinterpret_cast<char*>(_batch_out[fi].data.data()) + offset_src,
add_byte);
} else { // overwrite
if (dst->count() <= 0) {
dst->push_back(_batch_out[fi]);
} else {
(*dst)[fi] = _batch_out[fi];
}
(*dst)[fi].shape[0] = add;
(*dst)[fi].data = DataBuf(
reinterpret_cast<char*>(_batch_out[fi].data.data()) + offset_src,
add_byte);
}
}
}
for (size_t ti = 0; ti < _tasks.size(); ++ti) {
TaskT* task = _tasks[ti].task;
size_t begin = _tasks[ti].begin;
size_t end = _tasks[ti].end;
size_t add = end - begin;
size_t index = task->index.fetch_add(add);
if ((index + add) >= task->in->size()) {
char c = 0;
while (write(task->write_fd, &c, 1) != 1 && errno == EINTR) {
}
butil::return_object(task);
}
}
}
const typename TaskT::InArrayT& in() const { return _batch_in; }
typename TaskT::OutArrayT& out() { return _batch_out; }
size_t task_size() { return _tasks.size(); }
private:
std::vector<TaskMetaT> _tasks;
InArrayT _batch_in;
OutArrayT _batch_out;
size_t _batch_size;
size_t _rem_size;
bool _batch_align;
};
} // namespace bsf
} // namespace im
core/predictor/framework/bsf-inl.h
浏览文件 @ fe0ab9c8
......@@ -24,6 +24,7 @@
#include <boost/bind.hpp>
#include "core/predictor/common/inner_common.h"
#include "core/predictor/framework/memory.h"
namespace im {
namespace bsf {
......@@ -35,7 +36,7 @@ void* TaskExecutor<TaskT>::thread_entry(void* args) {
static_cast<TaskExecutor<TaskT>*>(context->executor);
executor->work(context);
return NULL;
return nullptr;
}
template <typename TaskT>
......@@ -125,18 +126,21 @@ void TaskExecutor<TaskT>::stop() {
}
template <typename TaskT>
TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(const InArrayT& in,
OutArrayT& out) { // NOLINT
TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(
const void* inVectorT_ptr,
void* outVectorT_ptr) { // NOLINT
TaskT* task = butil::get_object<TaskT>();
if (!task) {
LOG(ERROR) << "Failed get TaskT from object pool";
return TaskHandler<TaskT>::valid_handle();
}
/*
if (!BatchTasks<TaskT>::check_valid(in, out, _batch_align)) {
LOG(ERROR) << "Invalid input & output";
return TaskHandler<TaskT>::valid_handle();
}
*/
int fds[2];
int rc = pipe(fds);
......@@ -150,10 +154,9 @@ TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(const InArrayT& in,
task->write_fd = fds[1];
task->owner_tid = ::syscall(SYS_gettid);
task->in = &in;
task->out = &out;
task->rem = in.size();
task->size = in.size();
task->inVectorT_ptr = (const InVectorT*)inVectorT_ptr;
task->outVectorT_ptr = (OutVectorT*)outVectorT_ptr;
task->rem = task->batch_size();
task->index.store(0, butil::memory_order_relaxed);
AutoMutex lock(_mut);
......@@ -163,8 +166,13 @@ TaskHandler<TaskT> TaskExecutor<TaskT>::schedule(const InArrayT& in,
return TaskHandler<TaskT>(*task);
}
// this function is accessed by multi thread.
// so AutoMutex at first.
// so batch.append_task is thread safe.
// you dont need to add extra lock in append_task()
template <typename TaskT>
bool TaskExecutor<TaskT>::fetch_batch(BatchTasks<TaskT>& batch) { // NOLINT
bool TaskExecutor<TaskT>::move_task_to_batch(
BatchTasks<TaskT>& batch) { // NOLINT
AutoMutex lock(_mut);
while (_task_queue.empty()) {
THREAD_COND_WAIT(&_cond, &_mut);
......@@ -187,8 +195,30 @@ bool TaskExecutor<TaskT>::fetch_batch(BatchTasks<TaskT>& batch) { // NOLINT
return true;
}
// this function is accessed by multi thread.
// move_task_to_batch have add lock inside the function.
// Packaging 1 TaskT as 1 or Several TaskMeta.
// TaskT is from the SingleTon TaskExecutor`s _task_queue
// although TaskMeta is a local variable, but several TaskMeta may points to
// the same TaskT which is get from the SingleTon TaskExecutor`s _task_queue.
// put TaskMeta to the local variable BatchTasks<TaskT> batch.
// batch.merge_tasks() and batch.notify_tasks() has no lock.
// BatchTasks<TaskT> batch itself is a local variable, it`s thread safe.
// If batch.merge_tasks() and batch.notify_tasks() do something to TaskMeta
// you need to pay attention to that.
// Multi-Thread deal with different TaskMeta(cause it`s created as local
// variable)
// But different TaskMeta may points to the same TaskT
// which is get from the SingleTon TaskExecutor`s _task_queue.
template <typename TaskT>
int TaskExecutor<TaskT>::work(ThreadContext<TaskT>* context) {
if (MempoolWrapper::instance().thread_initialize() != 0) {
LOG(ERROR) << "Failed thread initialize mempool";
return -1;
}
if (_thread_init_fn != NULL) {
if (_thread_init_fn(context->user_thread_context) != 0) {
LOG(ERROR) << "execute thread init thunk failed, BSF thread will exit";
......@@ -207,10 +237,15 @@ int TaskExecutor<TaskT>::work(ThreadContext<TaskT>* context) {
}
}
if (MempoolWrapper::instance().thread_clear() != 0) {
LOG(ERROR) << "Failed thread clear mempool";
return -1;
}
BatchTasks<TaskT> batch(_batch_size, _batch_align);
if (fetch_batch(batch)) {
if (move_task_to_batch(batch)) {
batch.merge_tasks();
_fn(batch.in(), batch.out());
_fn(&batch.in(), &batch.out());
batch.notify_tasks();
}
}
......@@ -219,9 +254,10 @@ int TaskExecutor<TaskT>::work(ThreadContext<TaskT>* context) {
}
template <typename InItemT, typename OutItemT>
bool TaskManager<InItemT, OutItemT>::schedule(const InArrayT& in,
OutArrayT& out) { // NOLINT
TaskHandler<TaskT> handler = _executor.schedule(in, out);
bool TaskManager<InItemT, OutItemT>::schedule(const void* in,
void* out) { // NOLINT
TaskHandler<TaskT> handler =
TaskExecutorVector<TaskT>::instance()[_model_index].schedule(in, out);
if (handler.valid()) {
_task_owned = handler;
......
core/predictor/framework/bsf.h
浏览文件 @ fe0ab9c8
......@@ -16,7 +16,7 @@
#include <errno.h>
#include <algorithm>
#include <deque>
#include <list>
#include <vector>
#ifdef BCLOUD
......@@ -29,46 +29,186 @@
#include "boost/function.hpp"
#include "core/predictor/framework/memory.h"
#include "paddle_inference_api.h"
namespace im {
namespace bsf {
static const size_t DEFAULT_BATCH_SIZE = 100;
// InItemT is paddle::PaddleTensor
// InVectorT std::vector<paddle::PaddleTensor>
// InVectorT means different feedvar, but not batch.
// Batch is already inside the paddle::PaddleTensor.
// size_t `rem` records how many batch have not been put in BatchTasks.
// `rem` don`t need to be atomic, cause the operation `put` is synchronous.
// actually, the reason is that lock have been added outside the operation
// `put`.
// size_t `index` records how many batch have been processing completed.
// `index` need to be atomic, cause the operation 'notify' is asynchronous.
template <typename InItemT, typename OutItemT>
struct Task {
typedef std::vector<InItemT> InArrayT;
typedef std::vector<OutItemT> OutArrayT;
typedef std::vector<InItemT> InVectorT;
typedef std::vector<OutItemT> OutVectorT;
typedef InItemT InType;
typedef OutItemT OutType;
typedef Task<InItemT, OutItemT> TaskT;
typedef std::vector<int> ShapeVector;
typedef std::vector<ShapeVector> VectorOfShapeVector;
int read_fd;
int write_fd;
pid_t owner_tid;
const InArrayT* in;
OutArrayT* out;
const InVectorT* inVectorT_ptr;
OutVectorT* outVectorT_ptr;
size_t rem;
size_t size;
size_t batch_size() { return in->size(); }
butil::atomic<size_t> index;
Task() {
read_fd = -1;
write_fd = -1;
owner_tid = -1;
in = NULL;
out = NULL;
inVectorT_ptr = NULL;
outVectorT_ptr = NULL;
rem = -1;
size = -1;
index.store(0, butil::memory_order_relaxed);
}
bool check_feedvar_valid(int feedvar_index) {
if (feedvar_index < 0 || inVectorT_ptr->size() <= feedvar_index) {
LOG(ERROR) << "feedvar doesnt exsit or feedvar_index error";
return 0;
}
if ((*inVectorT_ptr)[feedvar_index].shape.size() <= 0) {
LOG(ERROR) << "feedvar[" << feedvar_index << "].shape.size()<=0,error";
return 0;
}
return 1;
}
// Now, it simply assume that the first dimension of data is batch.
// so the batch is PaddleTensor.shape[0]
// If batch information is added into feedvar.prototxt.
// we can get the information from the feedvar.prototxt instead of assume.
size_t feedvar_batch_size(int feedvar_index) {
if (!check_feedvar_valid(feedvar_index)) {
return 0;
}
return (*inVectorT_ptr)[feedvar_index].shape[0];
}
size_t feedvar_element_bytesize(int feedvar_index) {
if (!check_feedvar_valid(feedvar_index)) {
return 0;
}
int dtype = (*inVectorT_ptr)[feedvar_index].dtype;
if (dtype == paddle::PaddleDType::INT64) {
return sizeof(int64_t);
}
if (dtype == paddle::PaddleDType::FLOAT32) {
return sizeof(float);
}
if (dtype == paddle::PaddleDType::INT32) {
return sizeof(int32_t);
}
if (dtype == paddle::PaddleDType::UINT8) {
return sizeof(char);
}
return 0;
}
// Now, the implementation of this function is based on assumption
// that shape [0] = batch_size.
size_t feedvar_element_num(int feedvar_index) {
if (!check_feedvar_valid(feedvar_index)) {
return 0;
}
int element_num = 1;
if ((*inVectorT_ptr)[feedvar_index].shape.size() == 1) {
// cause shape[0] is batch_size.
// [10,1] = [10], so if shape[1] doesn`t exist.
// should return 1.
return 1;
}
// start from shape[1], cause shape[0] = batch_size.
for (int i = 1; i < (*inVectorT_ptr)[feedvar_index].shape.size(); ++i) {
element_num *= (*inVectorT_ptr)[feedvar_index].shape[i];
}
return element_num;
}
size_t feedvar_bytesize(int feedvar_index) {
return feedvar_element_num(feedvar_index) *
feedvar_element_bytesize(feedvar_index);
}
ShapeVector feedvar_shape_nobatch(int feedvar_index) {
if (!check_feedvar_valid(feedvar_index)) {
return ShapeVector();
}
return ShapeVector{(*inVectorT_ptr)[feedvar_index].shape.begin() + 1,
(*inVectorT_ptr)[feedvar_index].shape.end()};
}
VectorOfShapeVector feedvar_shape_nobatch() {
VectorOfShapeVector vector_of_feedvar_shape_nobatch(inVectorT_ptr->size());
for (int index = 0; index < inVectorT_ptr->size(); ++index) {
vector_of_feedvar_shape_nobatch.push_back(feedvar_shape_nobatch(index));
}
return vector_of_feedvar_shape_nobatch;
}
// At present, it is considered that the batch of all feedvar is consistent.
// so for each feedvar, PaddleTensor.shape[0] should be the same.
bool check_batch_align() {
int batch_size_align = feedvar_batch_size(0);
for (int feedvar_index = 0; feedvar_index < inVectorT_ptr->size();
++feedvar_index) {
if (feedvar_batch_size(feedvar_index) != batch_size_align) {
return 0;
}
}
/*
for(int fetchvar_index = 0; fetchvar_index < outVectorT_ptr->size();
++fetchvar_index) {
if(fetchvar_batch_size(fetchvar_index) != batch_size_align) {
return 0;
}
}
*/
return 1;
}
size_t batch_size() {
if (check_batch_align()) {
return feedvar_batch_size(0);
}
return 0;
}
};
// `Several Task` or `part of batch in Task` can be a TaskMeta.
// Task is the original Request from User.
// For example, the batch of Task is 30. There are 4 Requests.
// The batch of BatchTasks is 100, which means we can deal 100 batch 1 time.
// TaskMeta-1:{task-1,0,30} TaskMeta-2:{task-2,0,30} TaskMeta-3:{task-3,0,30}
// but the last Task will be divided to 2 TaskMeta.
// TaskMeta-4:{task-4,0,10} TaskMeta-5:{task-4,10,30}.
// TaskMeta-1 ~ TaskMeta-4 will be inside BatchTasks-1.
// TaskMeta-5 will be inside BatchTasks-2.
// TaskMeta is necessary.
// cause we need know the the corresponding relationship between
// `batch_out`(which is in BatchTasks) and `outVectorT_ptr`(which is in Task).
// especially when 1 Task be divided into several TaskMeta and be put into
// several different BatchTasks.
template <typename TaskT>
struct TaskMeta {
TaskMeta(TaskT* ptr, size_t start, size_t add)
......@@ -79,6 +219,11 @@ struct TaskMeta {
size_t end;
};
// each TaskT is already include batch in itself
// BatchTasks need to combine several `small TaskMeta` into a new `big TaskT`.
// The only difference between the `big TaskT` and `small TaskT` is that
// the TaskT.inVectorT_ptr->[feedvar_index].shape[0]
// which is actually batch_size is different.
template <typename TaskT>
class BatchTasks {
public:
......@@ -91,33 +236,38 @@ class BatchTasks {
_rem_size(batch_size),
_batch_align(batch_align) {
_batch_in.clear();
_batch_in_offset.clear();
_batch_out.clear();
_tasks.clear();
_batch_out_offset.clear();
_taskmeta_vector.clear();
}
~BatchTasks() {
_batch_in.clear();
_batch_in_offset.clear();
_batch_out.clear();
_tasks.clear();
_batch_out_offset.clear();
_taskmeta_vector.clear();
}
// synchronized operation
// because Upper level callers of this function have already locked.
size_t append_task(TaskT* task) {
size_t add = std::min(task->rem, _rem_size);
if (!_batch_align) {
add = task->rem;
}
TaskMetaT tm(task, task->in->size() - task->rem, add);
_tasks.push_back(tm);
int start_index = task->batch_size() - task->rem;
TaskMetaT tm(task, start_index, add);
_taskmeta_vector.push_back(tm);
task->rem -= add;
_rem_size -= add;
return _rem_size;
}
static bool check_valid(const typename TaskT::InArrayT& in,
const typename TaskT::OutArrayT& out,
static bool check_valid(const typename TaskT::InVectorT& in,
const typename TaskT::OutVectorT& out,
bool align) {
(void)in;
(void)out;
......@@ -125,40 +275,221 @@ class BatchTasks {
return true;
}
// this should be modified totally.
// maybe we don`t need to do this inside the BatchTasks.
// we can do the copy work outside the BatchTasks.
// cause maybe next time we don`t need to do the extra copy.
// directly copy the every Task into the Predictor.
// lod is not supported.
// if lod is set, we should not allow to use the bsf task.
// batch.merge_tasks() is thread-safe function
// cause batch is a local variable and Task is just read, not written.
void merge_tasks() {
for (size_t ti = 0; ti < _tasks.size(); ++ti) {
TaskMetaT& tm = _tasks[ti];
for (size_t vi = tm.begin; vi < tm.end; ++vi) {
_batch_in.push_back((*tm.task->in)[vi]);
_batch_out.push_back((*tm.task->out)[vi]);
if (_taskmeta_vector.size() <= 0) {
return;
}
// Temporarily, the batch of each feedvar is consistent
// If not consistent, use feedvar_batch_size instead of task->batch_size().
int temp_batch = 0;
for (size_t ti = 0; ti < _taskmeta_vector.size(); ++ti) {
TaskMetaT& tm = _taskmeta_vector[ti];
temp_batch += tm.task->batch_size();
}
if (temp_batch > _batch_size) {
LOG(ERROR) << "_realNumber_batch_in >_batch_size, error.";
return;
}
int feedvar_num = _taskmeta_vector[0].task->inVectorT_ptr->size();
if (_batch_in_offset.size() == 0) {
_batch_in_offset.resize(feedvar_num, 0);
_realNumber_batch_in.resize(feedvar_num, temp_batch);
}
for (size_t ti = 0; ti < _taskmeta_vector.size(); ++ti) {
TaskMetaT& tm = _taskmeta_vector[ti];
for (int index = 0; index < feedvar_num; ++index) {
const paddle::PaddleTensor& feedVarTensor =
(*tm.task->inVectorT_ptr)[index];
int feedvar_bytesize = tm.task->feedvar_bytesize(index);
if (ti == 0) {
if (feedVarTensor.lod.size() > 0 && feedVarTensor.lod[0].size() > 0) {
LOG(ERROR) << "lod Tensor is not supported now.";
return;
}
// for now, we assume that every task feedvar_bytesize is the same.
// which means we dont support auto embedding.
// but for different feedvar, it is different.
paddle::PaddleTensor paddleTensor;
paddleTensor.dtype = feedVarTensor.dtype;
paddleTensor.name = feedVarTensor.name;
paddleTensor.lod = feedVarTensor.lod;
paddleTensor.shape = feedVarTensor.shape;
paddleTensor.shape[0] = _realNumber_batch_in[index];
paddleTensor.data.Resize(feedvar_bytesize *
_realNumber_batch_in[index]);
_batch_in.push_back(paddleTensor);
}
void* dst_ptr = _batch_in[index].data.data() +
feedvar_bytesize * _batch_in_offset[index];
void* source_ptr =
feedVarTensor.data.data() + feedvar_bytesize * tm.begin;
int length = feedvar_bytesize * (tm.end - tm.begin);
memcpy(dst_ptr, source_ptr, length);
_batch_in_offset[index] += length;
}
}
}
bool check_fetchvar_valid(int fetchvar_index) {
if (fetchvar_index < 0 || _batch_out.size() <= fetchvar_index) {
LOG(ERROR) << "fetchvar doesnt exsit or fetchvar_index error";
return 0;
}
if (_batch_out[fetchvar_index].shape.size() <= 0) {
LOG(ERROR) << "fetchvar[" << fetchvar_index << "].shape.size()<=0,error";
return 0;
}
return 1;
}
size_t fetchvar_batch_size(int fetchvar_index) {
if (!check_fetchvar_valid(fetchvar_index)) {
return 0;
}
return _batch_out[fetchvar_index].shape[0];
}
size_t fetchvar_element_bytesize(int fetchvar_index) {
if (!check_fetchvar_valid(fetchvar_index)) {
return 0;
}
int dtype = _batch_out[fetchvar_index].dtype;
if (dtype == paddle::PaddleDType::INT64) {
return sizeof(int64_t);
}
if (dtype == paddle::PaddleDType::FLOAT32) {
return sizeof(float);
}
if (dtype == paddle::PaddleDType::INT32) {
return sizeof(int32_t);
}
if (dtype == paddle::PaddleDType::UINT8) {
return sizeof(char);
}
return 0;
}
// Now, the implementation of this function is based on assumption
// that shape [0] = batch_size.
size_t fetchvar_element_num(int fetchvar_index) {
if (!check_fetchvar_valid(fetchvar_index)) {
return 0;
}
int element_num = 1;
if (_batch_out[fetchvar_index].shape.size() == 1) {
// cause shape[0] is batch_size.
return 1;
}
// start from shape[1], cause shape[0] = batch_size.
for (int i = 1; i < _batch_out[fetchvar_index].shape.size(); ++i) {
element_num *= _batch_out[fetchvar_index].shape[i];
}
return element_num;
}
size_t fetchvar_bytesize(int fetchvar_index) {
return fetchvar_element_num(fetchvar_index) *
fetchvar_element_bytesize(fetchvar_index);
}
bool check_fetchvar_batch_align() {
int batch_size_align = fetchvar_batch_size(0);
for (int fetchvar_index = 0; fetchvar_index < _batch_out.size();
++fetchvar_index) {
if (fetchvar_batch_size(fetchvar_index) != batch_size_align) {
return 0;
}
}
return 1;
}
size_t fetchvar_batch_size() {
if (check_fetchvar_batch_align()) {
return fetchvar_batch_size(0);
}
return 0;
}
void notify_tasks() {
if (_batch_out.size() != _batch_in.size()) {
LOG(ERROR) << "batch size not consistency: " << _batch_out.size()
<< " != " << _batch_in.size();
if (_taskmeta_vector.size() <= 0) {
LOG(ERROR) << "_taskmeta_vector.size() <=0, error.";
return;
}
if (_realNumber_batch_in[0] != fetchvar_batch_size()) {
LOG(ERROR) << "_batch_out`s batch != _batch_in`s batch, error.";
return;
}
for (size_t ti = 0, bi = 0; ti < _tasks.size(); ++ti) {
TaskT* task = _tasks[ti].task;
size_t begin = _tasks[ti].begin;
size_t end = _tasks[ti].end;
int fetchvar_num = _batch_out.size();
if (_batch_out_offset.size() == 0) {
_batch_out_offset.resize(fetchvar_num, 0);
}
for (size_t ti = 0; ti < _taskmeta_vector.size(); ++ti) {
TaskT* task = _taskmeta_vector[ti].task;
size_t begin = _taskmeta_vector[ti].begin;
size_t end = _taskmeta_vector[ti].end;
size_t add = end - begin;
for (size_t oi = begin; oi < end; ++oi, ++bi) {
if (bi >= _batch_in.size()) {
LOG(ERROR) << "batch index overflow: " << bi << " > "
<< _batch_in.size();
for (int index = 0; index < fetchvar_num; ++index) {
// the task->outVectorT_ptr is null before core->run().
// first time we should copy from _batch_out
// so we need init.
int fetchvar_bytesize_index = fetchvar_bytesize(index);
if (task->outVectorT_ptr->size() <= index) {
paddle::PaddleTensor tensor_out;
tensor_out.name = _batch_out[index].name;
tensor_out.dtype = paddle::PaddleDType(_batch_out[index].dtype);
tensor_out.shape = _batch_out[index].shape;
tensor_out.shape[0] = task->batch_size();
tensor_out.lod = _batch_out[index].lod;
// resize all batch memory at one time
size_t databuf_size = task->batch_size() * fetchvar_bytesize_index;
tensor_out.data.Resize(databuf_size);
task->outVectorT_ptr->push_back(tensor_out);
}
paddle::PaddleTensor& fetchVarTensor = (*task->outVectorT_ptr)[index];
void* dst_ptr =
fetchVarTensor.data.data() + fetchvar_bytesize_index * begin;
int length = fetchvar_bytesize_index * add;
if (_batch_out_offset[index] + length >
fetchvar_batch_size() * fetchvar_bytesize(index)) {
LOG(ERROR) << "_batch_out is less than taskmeta, error.";
return;
}
(*task->out)[oi] = _batch_out[bi];
void* source_ptr =
_batch_out[index].data.data() + _batch_out_offset[index];
memcpy(dst_ptr, source_ptr, length);
_batch_out_offset[index] += length;
}
size_t index = task->index.fetch_add(add);
if ((index + add) >= task->in->size()) {
if ((index + add) >= task->batch_size()) {
char c = 0;
while (write(task->write_fd, &c, 1) != 1 && errno == EINTR) {
}
......@@ -167,22 +498,33 @@ class BatchTasks {
}
}
const typename TaskT::InArrayT& in() const { return _batch_in; }
const typename TaskT::InVectorT& in() const { return _batch_in; }
typename TaskT::OutArrayT& out() { return _batch_out; }
typename TaskT::OutVectorT& out() { return _batch_out; }
size_t task_size() { return _tasks.size(); }
size_t task_size() { return _taskmeta_vector.size(); }
private:
std::vector<TaskMetaT> _tasks;
typename TaskT::InArrayT _batch_in;
typename TaskT::OutArrayT _batch_out;
std::vector<TaskMetaT> _taskmeta_vector;
typename TaskT::InVectorT _batch_in;
std::vector<int> _batch_in_offset;
std::vector<int> _realNumber_batch_in;
typename TaskT::OutVectorT _batch_out;
std::vector<int> _batch_out_offset;
std::vector<int> _realNumber_batch_out;
size_t _rem_size;
size_t _batch_size;
bool _batch_align;
};
// BSF task handle
// TaskHandler is the handle of Task.
// `read_fd` is used for receive signal in brpc Thread.
// 'write_fd' is used for write signal in bsf Thread.
// when TaskMeta is done, bsf Thread will write to 'write_fd'.
// brpc Thread is keeping reading 'read_fd' in a while loop.
// brpc Thread will receive signal when TaskMeta is done.
// so `read_fd` and 'write_fd' is used for communicate in different Thread.
template <typename TaskT>
struct TaskHandler {
int read_fd;
......@@ -205,12 +547,11 @@ struct TaskHandler {
}
};
// TaskExecutor is a Thread pool.
template <typename TaskT>
class TaskExecutor;
template <typename InItemT, typename OutItemT>
class TaskManager;
// ThreadContext is used for start a bsf Thread.
template <typename TaskT>
struct ThreadContext {
TaskExecutor<TaskT>* executor;
......@@ -231,14 +572,24 @@ struct ThreadContext {
}
};
// TaskExecutor is a Thread pool.
// Each Model corresponding to a Model.
// TaskT is actually a Request preprocessed by ReaderOp.
// TaskT will be divided as TaskMeta which will be
// put into _task_queue in brpc-Thread by schedule().
// TaskHander will be returned to brpc-Thread.
// start() function will create `thread_num` bsf Threads.
// every bsf Thread check the _task_queue and take TaskMeta from it.
// when a Task`s all TaskMeta is done, TaskHander will be noticed.
template <typename TaskT>
class TaskExecutor {
public:
typedef typename TaskT::InType InType;
typedef typename TaskT::OutType OutType;
typedef typename TaskT::InArrayT InArrayT;
typedef typename TaskT::OutArrayT OutArrayT;
typedef typename TaskT::InVectorT InVectorT;
typedef typename TaskT::OutVectorT OutVectorT;
typedef std::vector<TaskT> TaskArrayT;
typedef baidu::paddle_serving::predictor::MempoolWrapper MempoolWrapper;
TaskExecutor()
: _stop(false),
......@@ -258,9 +609,11 @@ class TaskExecutor {
THREAD_COND_DESTROY(&_cond);
}
static TaskExecutor<TaskT>* instance() {
static TaskExecutor<TaskT> singleton;
return &singleton;
// cause vector.resize will use copy or move construct.
TaskExecutor(TaskExecutor<TaskT>&& other) noexcept {
if (this != &other) {
TaskExecutor();
}
}
void set_batch_size(size_t batch_size) { _batch_size = batch_size; }
......@@ -277,8 +630,7 @@ class TaskExecutor {
_thread_reset_fn = reset_fn;
}
void set_thread_callback_fn(
boost::function<void(const InArrayT&, OutArrayT&)> cb) {
void set_thread_callback_fn(boost::function<void(const void*, void*)> cb) {
_fn = cb;
}
......@@ -287,15 +639,21 @@ class TaskExecutor {
static void* thread_entry(void* args);
private:
TaskExecutor(TaskExecutor<TaskT> const& other);
TaskExecutor* operator=(TaskExecutor<TaskT> const& other);
int work(ThreadContext<TaskT>* context);
TaskHandler<TaskT> schedule(const InArrayT&, OutArrayT&);
TaskHandler<TaskT> schedule(const void*, void*);
bool fetch_batch(BatchTasks<TaskT>& batch); // NOLINT
bool move_task_to_batch(BatchTasks<TaskT>& batch); // NOLINT
private:
TaskExecutor(TaskExecutor<TaskT> const& other) = delete;
TaskExecutor& operator=(TaskExecutor<TaskT> const& other) = delete;
/*
TaskExecutor(TaskExecutor<TaskT> && other) = delete;
TaskExecutor& operator=(TaskExecutor<TaskT> && other) = delete;
*/
bool _stop;
......@@ -303,43 +661,76 @@ class TaskExecutor {
THREAD_MUTEX_T _mut;
THREAD_COND_T _cond;
std::deque<TaskT*> _task_queue;
std::list<TaskT*> _task_queue;
boost::function<int(void*)> _thread_init_fn;
boost::function<int(void*)> _thread_reset_fn;
void** _user_thread_contexts;
std::vector<ThreadContext<TaskT>*> _thread_contexts;
friend class TaskManager<InType, OutType>;
size_t _batch_size;
bool _batch_align;
boost::function<void(const InArrayT&, OutArrayT&)> _fn;
boost::function<void(const void*, void*)> _fn;
};
// TaskExecutorVector is a SingleTon class.
// Each Model corresponding to a TaskExecutor.
// So we need several TaskExecutor when there are more than 1 Model.
template <typename TaskT>
class TaskExecutorVector {
public:
static TaskExecutorVector<TaskT>& instance() {
static TaskExecutorVector<TaskT> singleton;
return singleton;
}
void resize(int size) { _vector_executor.resize(size); }
TaskExecutor<TaskT>& operator[](int index) {
if (_vector_executor.size() <= index || index <= -1) {
LOG(ERROR) << "_vector_executor.size() <= index or <= -1";
throw "_vector_executor.size() <= index or <= -1";
}
return _vector_executor[index];
}
private:
TaskExecutorVector() = default;
TaskExecutorVector(const TaskExecutorVector<TaskT>& other) = delete;
TaskExecutorVector& operator=(const TaskExecutorVector<TaskT>& other) =
delete;
TaskExecutorVector(TaskExecutorVector<TaskT>&& other) = delete;
TaskExecutorVector& operator=(TaskExecutorVector<TaskT>&& other) = delete;
std::vector<TaskExecutor<TaskT>> _vector_executor;
};
// TaskManager is actually a wrapper of Request in bsf.
// TaskManager`s schedule() change Request to be TaskT.
// and divided TaskT into several TaskMeta to put into the TaskExecutor`s
// task_queue.
// wait() is a while loop to receive signal when a whole Task is done.
template <typename InItemT, typename OutItemT>
class TaskManager {
public:
typedef Task<InItemT, OutItemT> TaskT;
typedef typename TaskT::InArrayT InArrayT;
typedef typename TaskT::OutArrayT OutArrayT;
explicit TaskManager(TaskExecutor<TaskT>& exe, size_t batch_size) // NOLINT
: _executor(exe) {}
typedef typename TaskT::InVectorT InVectorT;
typedef typename TaskT::OutVectorT OutVectorT;
TaskManager() : _executor(*TaskExecutor<TaskT>::instance()) {}
explicit TaskManager(uint32_t index) // NOLINT
: _model_index(index) {}
~TaskManager() { wait(); }
bool schedule(const InArrayT& in, OutArrayT& out); // NOLINT
bool schedule(const void* in, void* out); // NOLINT
void wait();
inline void clear() { wait(); }
private:
TaskExecutor<TaskT>& _executor;
TaskHandler<TaskT> _task_owned;
uint32_t _model_index;
}; // class TaskManager
class AutoMutex {
......@@ -357,5 +748,5 @@ class AutoMutex {
} // namespace bsf
} // namespace im
#include "core/predictor/framework/bsf-inl-tensor.h"
// #include "core/predictor/framework/bsf-inl-tensor.h"
#include "core/predictor/framework/bsf-inl.h"
core/predictor/framework/infer.cpp
浏览文件 @ fe0ab9c8
......@@ -56,15 +56,23 @@ int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
}
// init bsf framework
im::bsf::TaskExecutor<TaskT>::instance()->set_thread_init_fn(
boost::bind(&InferEngine::thrd_initialize_impl, this));
im::bsf::TaskExecutor<TaskT>::instance()->set_thread_reset_fn(
boost::bind(&InferEngine::thrd_clear_impl, this));
im::bsf::TaskExecutor<TaskT>::instance()->set_thread_callback_fn(
boost::bind(&InferEngine::task_infer_impl, this, _1, _2));
im::bsf::TaskExecutor<TaskT>::instance()->set_batch_size(_infer_batch_size);
im::bsf::TaskExecutor<TaskT>::instance()->set_batch_align(_infer_batch_align);
if (im::bsf::TaskExecutor<TaskT>::instance()->start(_infer_thread_num) != 0) {
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
.set_thread_init_fn(
boost::bind(&InferEngine::thrd_initialize_impl, this));
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
.set_thread_init_fn(
boost::bind(&InferEngine::thrd_initialize_impl, this));
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
.set_thread_reset_fn(boost::bind(&InferEngine::thrd_clear_impl, this));
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index]
.set_thread_callback_fn(
boost::bind(&InferEngine::task_infer_impl, this, _1, _2));
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].set_batch_size(
_infer_batch_size);
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].set_batch_align(
_infer_batch_align);
if (im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].start(
_infer_thread_num) != 0) {
LOG(ERROR) << "Failed start bsf executor, threads:" << _infer_thread_num;
return -1;
}
......@@ -75,6 +83,11 @@ int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
return 0;
}
// Multiple threads will enter this method of the same object
// One Model corresponds to One ReloadableInferEngine object.
// ReloadableInferEngine object is Process object.
// One ReloadableInferEngine object can have several ModelData<EngineCore>
// ModelData<EngineCore> is Thread object.
int ReloadableInferEngine::infer(const void* in,
void* out,
uint32_t batch_size) {
......@@ -82,9 +95,10 @@ int ReloadableInferEngine::infer(const void* in,
return infer_impl(in, out, batch_size);
}
im::bsf::TaskManager<Tensor, Tensor> task_manager;
task_manager.schedule(*(reinterpret_cast<const BatchTensor*>(in)),
*(reinterpret_cast<BatchTensor*>(out)));
im::bsf::TaskManager<paddle::PaddleTensor, paddle::PaddleTensor> task_manager(
_model_index);
task_manager.schedule(in, out);
task_manager.wait();
return 0;
}
......@@ -110,7 +124,7 @@ int ReloadableInferEngine::proc_finalize() {
}
if (_infer_thread_num > 0) {
im::bsf::TaskExecutor<TaskT>::instance()->stop();
im::bsf::TaskExecutorVector<TaskT>::instance()[_model_index].stop();
}
return 0;
}
......@@ -191,6 +205,7 @@ int VersionedInferEngine::proc_initialize(const configure::EngineDesc& conf,
std::string engine_type = conf.type();
InferEngine* engine =
StaticInferFactory::instance().generate_object(engine_type);
engine->set_model_index(_model_index);
if (!engine) {
LOG(ERROR) << "Failed generate engine with type:" << engine_type;
return -1;
......@@ -362,8 +377,8 @@ int VersionedInferEngine::infer_impl(const void* in,
uint32_t batch_size) {
return -1;
}
int VersionedInferEngine::task_infer_impl(const BatchTensor& in,
BatchTensor& out) { // NOLINT
int VersionedInferEngine::task_infer_impl(const void* in,
void* out) { // NOLINT
return -1;
}
......@@ -373,12 +388,14 @@ int InferManager::proc_initialize(const char* path, const char* file) {
LOG(ERROR) << "failed load infer config, path: " << path << "/" << file;
return -1;
}
size_t engine_num = model_toolkit_conf.engines_size();
for (size_t ei = 0; ei < engine_num; ++ei) {
uint32_t engine_num = model_toolkit_conf.engines_size();
im::bsf::TaskExecutorVector<TaskT>::instance().resize(engine_num);
for (uint32_t ei = 0; ei < engine_num; ++ei) {
LOG(INFO) << "model_toolkit_conf.engines(" << ei
<< ").name: " << model_toolkit_conf.engines(ei).name();
std::string engine_name = model_toolkit_conf.engines(ei).name();
VersionedInferEngine* engine = new (std::nothrow) VersionedInferEngine();
engine->set_model_index(ei);
if (!engine) {
LOG(ERROR) << "Failed generate versioned engine: " << engine_name;
return -1;
......
core/predictor/framework/infer.h 100755 → 100644
浏览文件 @ fe0ab9c8
......@@ -17,6 +17,7 @@
#include <sys/stat.h>
#include <sys/types.h>
#include <unistd.h>
#include <functional>
#include <numeric>
#include <string>
#include <utility>
......@@ -25,6 +26,7 @@
#include "core/predictor/framework/bsf.h"
#include "core/predictor/framework/factory.h"
#include "core/predictor/framework/infer_data.h"
#include "core/predictor/framework/memory.h"
#include "paddle_inference_api.h" // NOLINT
namespace baidu {
namespace paddle_serving {
......@@ -71,7 +73,7 @@ class InferEngine {
virtual int infer(const void* in, void* out, uint32_t batch_size = -1) {
return infer_impl(in, out, batch_size);
}
virtual void set_model_index(uint32_t index) { _model_index = index; }
virtual int reload() = 0;
virtual uint64_t version() const = 0;
......@@ -86,12 +88,13 @@ class InferEngine {
virtual int infer_impl(const void* in,
void* out,
uint32_t batch_size = -1) = 0;
virtual int task_infer_impl(const BatchTensor& in,
BatchTensor& out) = 0; // NOLINT
virtual int task_infer_impl(const void* in, void* out) = 0; // NOLINT
protected:
uint32_t _model_index;
// end: framework inner call
};
typedef im::bsf::Task<paddle::PaddleTensor, paddle::PaddleTensor> TaskT;
class ReloadableInferEngine : public InferEngine {
public:
virtual ~ReloadableInferEngine() {}
......@@ -104,7 +107,6 @@ class ReloadableInferEngine : public InferEngine {
};
virtual int load(const configure::EngineDesc& conf) = 0;
typedef im::bsf::Task<Tensor, Tensor> TaskT;
int proc_initialize_impl(const configure::EngineDesc& conf, bool version);
......@@ -179,6 +181,8 @@ struct ModelData {
delete cores[1];
}
void* get() { return cores[current_idx]->get(); }
EngineCore* cores[2];
uint32_t current_idx;
};
......@@ -191,14 +195,20 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
int proc_initialize(const configure::EngineDesc& conf, bool version) {
THREAD_KEY_CREATE(&_skey, NULL);
THREAD_MUTEX_INIT(&_mutex, NULL);
gpu_index = 0;
return ReloadableInferEngine::proc_initialize(conf, version);
}
// 进程初始化会调用load，但由于未执行线程初始化，所以_reload_vec为空,不再继续执行。
// 热加载的话会调用load，由于线程已经初始化，_reload_vec不为空，所以继续执行load_data操作加载数据。
// 线程初始化会执行load_data操作加载数据，然后将engine加入_reload_vec中。
// 每个模型只有一个CloneDBReloadableInferEngine对象。
// 但一个CloneDBReloadableInferEngine对象，可以包含N个EngineCore。
virtual int load(const configure::EngineDesc& conf) {
if (_reload_vec.empty()) {
return 0;
}
gpu_index = 0;
for (uint32_t ti = 0; ti < _reload_vec.size(); ++ti) {
if (load_data(_reload_vec[ti], conf) != 0) {
LOG(ERROR) << "Failed reload engine model: " << ti;
......@@ -210,7 +220,8 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
return 0;
}
int load_data(ModelData<EngineCore>* md, const configure::EngineDesc& conf) {
virtual int load_data(ModelData<EngineCore>* md,
const configure::EngineDesc& conf) {
uint32_t next_idx = (md->current_idx + 1) % 2;
if (md->cores[next_idx]) {
delete md->cores[next_idx];
......@@ -219,28 +230,29 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
md->cores[next_idx] = new (std::nothrow) EngineCore;
// params.dump();
if (!md->cores[next_idx] || md->cores[next_idx]->create(conf) != 0) {
size_t gpu_ids_num = conf.gpu_ids_size();
im::bsf::AutoMutex lock(_mutex);
int gpu_id = -1;
if (gpu_ids_num > 0) {
gpu_id = conf.gpu_ids(gpu_index % gpu_ids_num);
}
if (!md->cores[next_idx] ||
md->cores[next_idx]->create(conf, gpu_id) != 0) {
LOG(ERROR) << "Failed create model, path: " << conf.model_dir();
return -1;
}
gpu_index++;
md->current_idx = next_idx;
return 0;
}
virtual int thrd_initialize_impl() {
// memory pool to be inited in non-serving-threads
if (MempoolWrapper::instance().thread_initialize() != 0) {
LOG(ERROR) << "Failed thread initialize mempool";
return -1;
}
ModelData<EngineCore>* md = new (std::nothrow) ModelData<EngineCore>;
if (!md || load_data(md, _conf) != 0) {
LOG(ERROR) << "Failed create thread data from " << _conf.model_dir();
return -1;
}
LOG(ERROR) << "THREAD_SETSPECIFIC _skey = md";
THREAD_SETSPECIFIC(_skey, md);
im::bsf::AutoMutex lock(_mutex);
_reload_vec.push_back(md);
......@@ -248,11 +260,33 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
}
int thrd_clear_impl() {
// for non-serving-threads
if (MempoolWrapper::instance().thread_clear() != 0) {
LOG(ERROR) << "Failed thread clear mempool";
return -1;
}
// actually, there are 2 kinds of multi-thread.
// 1. brpc thread 2. bsf Task thread
// each request is in 1-single brpc thread.
// IF (bsf Task thread is not used)
// every single brpc thread corresponds to all the DBReloadableInferEngines.
// each request runs all models in 1-single brpc thread.
// every single brpc thread will create or clone N predictor.
// N = the number of Model.
// so if there are 2 models, and --thread 10.
// each brpc thread will create predictor of Model-1 and Model-2.
// there are totally 10 predictors of Model-1 and 10 predictors of Model-2
// cause there are 10 brpc threads.
// IF bsf Task thread is used。
// there will be a ThreadPool called bsf TaskExecutor.
// TaskExecutorVector is the vector of TaskExecutor.
// the number of TaskExecutor equals to the number of Model.
// 1 TaskExecutor corresponding to 1 Model.
// 1 TaskExecutor have N bsf threads.
// 1 bsf thread corresponds to 1 predictor of
// the Model corresponding to the TaskExecutor.
// brpc thread only put the data into the task_queue(which is in
// TaskExecutor)
// EngineCore->infer() is running in bsf Task thread.
// MempoolWrapper::instance() is actually a Thread-Local Mempool.
// so it belongs to a single Thread.
return 0;
}
......@@ -278,6 +312,7 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
THREAD_KEY_T _skey;
THREAD_MUTEX_T _mutex;
std::vector<ModelData<EngineCore>*> _reload_vec;
int gpu_index = 0;
};
// 多个EngineCore共用同一份模型数据
......@@ -287,88 +322,72 @@ class CloneDBReloadableInferEngine
public:
virtual ~CloneDBReloadableInferEngine() {}
virtual int proc_initialize(const configure::EngineDesc& conf, bool version) {
_pd = new (std::nothrow) ModelData<EngineCore>;
if (!_pd) {
LOG(ERROR) << "Failed to allocate for ProcData";
return -1;
}
return DBReloadableInferEngine<EngineCore>::proc_initialize(conf, version);
}
// 进程初始化会调用load，但由于未执行线程初始化，所以_reload_vec为空,不再继续执行。
// 热加载的话会调用load，由于线程已经初始化，_reload_vec不为空，所以继续执行load_data操作加载数据。
// 线程初始化会执行load_data操作加载数据，然后将engine加入_reload_vec中。
// 每个模型只有一个CloneDBReloadableInferEngine对象。
// 但一个CloneDBReloadableInferEngine对象，可以包含N个EngineCore。
virtual int load(const configure::EngineDesc& conf) {
// 加载进程级模型数据
if (!_pd ||
DBReloadableInferEngine<EngineCore>::load_data(_pd, conf) != 0) {
LOG(ERROR) << "Failed to create common model from [" << conf.model_dir()
<< "].";
return -1;
virtual int load_data(ModelData<EngineCore>* md,
const configure::EngineDesc& conf) {
uint32_t next_idx = (md->current_idx + 1) % 2;
if (md->cores[next_idx]) {
delete md->cores[next_idx];
}
LOG(WARNING) << "Succ load common model[" << _pd->cores[_pd->current_idx]
<< "], path[" << conf.model_dir() << "].";
md->cores[next_idx] = new (std::nothrow) EngineCore;
if (DBReloadableInferEngine<EngineCore>::_reload_vec.empty()) {
return 0;
// params.dump();
size_t gpu_ids_num = conf.gpu_ids_size();
im::bsf::AutoMutex lock(DBReloadableInferEngine<EngineCore>::_mutex);
int gpu_id = -1;
if (gpu_ids_num > 0) {
gpu_id = conf.gpu_ids(DBReloadableInferEngine<EngineCore>::gpu_index %
gpu_ids_num);
}
for (uint32_t ti = 0;
ti < DBReloadableInferEngine<EngineCore>::_reload_vec.size();
++ti) {
if (load_data(DBReloadableInferEngine<EngineCore>::_reload_vec[ti],
_pd->cores[_pd->current_idx]) != 0) {
LOG(ERROR) << "Failed reload engine model: " << ti;
// gpu_index will be set to be 0, when load() or proc_initial() is called.
// gpu_index < gpu_ids_num, means there are predictors still not create
// on some GPU card.
// so we need to create the predictor.
// gpu_index >= gpu_ids_num, means each GPU card has already create one.
// so we need to clone the predictor.
if (DBReloadableInferEngine<EngineCore>::gpu_index < gpu_ids_num) {
if (!md->cores[next_idx] ||
md->cores[next_idx]->create(conf, gpu_id) != 0) {
LOG(ERROR) << "Failed create model, path: " << conf.model_dir();
return -1;
}
DBReloadableInferEngine<EngineCore>::gpu_index++;
md->current_idx = next_idx;
if (_cloneTemplate.size() <
DBReloadableInferEngine<EngineCore>::gpu_index) {
_cloneTemplate.push_back(md);
} else {
_cloneTemplate[DBReloadableInferEngine<EngineCore>::gpu_index - 1] = md;
}
} else {
// when gpu_id = -1, means we use cpu, but the index should be 0.
// _cloneTemplate[-1] will occur error.
// actually, when gpu_id = -1, there is only 1 predictor in
// _cloneTemplate.
// so the index should always be 0 when gpu_id = -1.
if (gpu_id == -1) gpu_id = 0;
if (!md->cores[next_idx] ||
md->cores[next_idx]->clone(_cloneTemplate[gpu_id]->get()) != 0) {
LOG(ERROR) << "Failed clone model from core";
return -1;
}
DBReloadableInferEngine<EngineCore>::gpu_index++;
md->current_idx = next_idx;
LOG(WARNING) << "core clone model succ, cur_idx[" << md->current_idx
<< "].";
}
LOG(WARNING) << "Succ load clone model, path[" << conf.model_dir() << "]";
return 0;
}
// 加载线程级对象，多个线程级对象共用pd_core的模型数据
int load_data(ModelData<EngineCore>* td, EngineCore* pd_core) {
uint32_t next_idx = (td->current_idx + 1) % 2;
if (td->cores[next_idx]) {
delete td->cores[next_idx];
}
td->cores[next_idx] = new (std::nothrow) EngineCore;
if (!td->cores[next_idx] ||
td->cores[next_idx]->clone(pd_core->get()) != 0) {
LOG(ERROR) << "Failed clone model from pd_core[ " << pd_core << "], idx["
<< next_idx << "]";
return -1;
}
td->current_idx = next_idx;
LOG(WARNING) << "td_core[" << td->cores[td->current_idx]
<< "] clone model from pd_core[" << pd_core
<< "] succ, cur_idx[" << td->current_idx << "].";
return 0;
}
virtual int thrd_initialize_impl() {
// memory pool to be inited in non-serving-threads
if (MempoolWrapper::instance().thread_initialize() != 0) {
LOG(ERROR) << "Failed thread initialize mempool";
return -1;
}
ModelData<EngineCore>* md = new (std::nothrow) ModelData<EngineCore>;
if (!md || load_data(md, _pd->cores[_pd->current_idx]) != 0) {
LOG(ERROR) << "Failed clone thread data, origin_core["
<< _pd->cores[_pd->current_idx] << "].";
return -1;
}
THREAD_SETSPECIFIC(DBReloadableInferEngine<EngineCore>::_skey, md);
im::bsf::AutoMutex lock(DBReloadableInferEngine<EngineCore>::_mutex);
DBReloadableInferEngine<EngineCore>::_reload_vec.push_back(md);
return 0;
}
protected:
ModelData<EngineCore>*
_pd; // 进程级EngineCore，多个线程级EngineCore共用该对象的模型数据
// 模板EngineCore，如果已创建，则多个线程级EngineCore共用该对象的模型数据
std::vector<ModelData<EngineCore>*> _cloneTemplate;
};
template <typename EngineCore>
......@@ -505,8 +524,8 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<EngineCore> {
return 0;
}
int task_infer_impl(const BatchTensor& in, BatchTensor& out) { // NOLINT
return infer_impl(&in, &out);
int task_infer_impl(const void* in, void* out) { // NOLINT
return infer_impl(in, out);
}
};
......@@ -559,7 +578,7 @@ class VersionedInferEngine : public InferEngine {
int infer_impl(const void* in, void* out, uint32_t batch_size = -1);
int task_infer_impl(const BatchTensor& in, BatchTensor& out);
int task_infer_impl(const void* in, void* out);
private:
boost::unordered_map<uint64_t, InferEngine*> _versions;
......
core/predictor/framework/server.cpp 100644 → 100755
浏览文件 @ fe0ab9c8
......@@ -91,6 +91,7 @@ int ServerManager::start_and_wait() {
}
}
// rpc multi-thread start from here.
if (_server.Start(FLAGS_port, &_options) != 0) {
LOG(ERROR) << "Failed to start Paddle Inference Server";
return -1;
......
core/predictor/mempool/mempool.cpp 100644 → 100755
浏览文件 @ fe0ab9c8
......@@ -24,7 +24,7 @@ namespace fugue {
namespace memory {
void Region::init() {
_big_mem_capacity = 64 * 1024 * 1024; // 64MB
_big_mem_capacity = 128 * 1024 * 1024; // 128MB
_big_mem_start = new char[_big_mem_capacity];
}
......
core/predictor/mempool/mempool.h
浏览文件 @ fe0ab9c8
......@@ -129,7 +129,7 @@ class FreeList {
to get the class Pointer
for example
T is the member of class Node, T data, 'data' is the name.
T* value is the member(pointer type) class Node
T* value is the member(pointer type) of class Node
so we can get the Node* by calling container_of(value, Node, data)
*/
Node* node = container_of(value, Node, data);
......@@ -261,7 +261,11 @@ struct BlockReference {
// because BlockFreeList is a threal-safe Singleton.
// so we don`t release Block, it is global memory.
// total number is 32*1024
// total number is 256*1024.
// the MAX_BLOCK_COUNT of Region(one thread one Region) is 1024.
// so BlockFreeList allow 256 Region(means 256 thread).
// the memory used by BlockFreeListType is sizeof(void*)*256*1024.
// Block(2MB) memory is created only when get() is called.
class BlockFreeList {
public:
static const int MAX_BLOCK_COUNT = 256 * 1024;
......@@ -341,9 +345,10 @@ class Region {
2 * 1024 *
1024; // 2MB,means when you need less than 2M, get memory from Block.
// 64MB,means when you need less than 64MB, get memory from BigMemory instead
// 128MB,means when you need less than 128MB, get memory from BigMemory
// instead
// of BigNode
static const int BIGNODE_MEM_THRESHOLD = (64 * 1024 * 1024 + 1);
static const int BIGNODE_MEM_THRESHOLD = (128 * 1024 * 1024 + 1);
static const int COUNTER_SIZE =
BIGNODE_MEM_THRESHOLD / BIG_MEM_THRESHOLD + 1; // this is not used
......@@ -374,7 +379,8 @@ class Mempool {
void* malloc(size_t size) {
size = _align(size);
// It does not enter the if statement the first time.
// Because the block has not been used up, it will enter.
// The if statement may enter after the block is created.
// If the block has not been used up, it will enter.
if (size <= _free_size) {
void* p = _free_cursor;
_free_size -= size;
......@@ -392,7 +398,7 @@ class Mempool {
return;
}
// memory in Block，update the pointer.
// memory in _block，update the pointer.
if (_free_cursor - size == static_cast<char*>(p)) {
// for example, you need to release -(8+1)bytes
// you can only release -8bytes，cause -(8+2)byte is used by other.
......@@ -424,9 +430,8 @@ class Mempool {
}
// 可能返回的是单独Region中malloc的内存。
// 也可能是Block，例如new_size=1M, old_data原本的指针头就在1.2M处，old_size
// =
// 0.5M
// 也可能是Block，例如new_size=1M, old_data原本的指针头就在1.2M处
// old_size = 0.5M
// 此时,_free_size = 0.3M，new_size<2M,但是required = 1-0.5 >0.3
// 分配出来的就是Block，但是该Block没有并很完美的利用完全。
void* p = this->malloc_from_region(new_size);
......
core/predictor/src/pdserving.cpp 100755 → 100644
浏览文件 @ fe0ab9c8
......@@ -68,13 +68,14 @@ static bvar::PassiveStatus<std::string> s_predictor_revision(
DEFINE_bool(V, false, "print version, bool");
DEFINE_bool(g, false, "user defined gflag path");
DECLARE_string(flagfile);
/*
namespace bthread {
extern pthread_mutex_t g_task_control_mutex;
}
pthread_mutex_t g_worker_start_fn_mutex = PTHREAD_MUTEX_INITIALIZER;
*/
void pthread_worker_start_fn() {
/*
while (pthread_mutex_lock(&g_worker_start_fn_mutex) != 0) {
}
......@@ -83,15 +84,18 @@ void pthread_worker_start_fn() {
if (lock_status == EBUSY || lock_status == EAGAIN) {
pthread_mutex_unlock(&bthread::g_task_control_mutex);
}
*/
Resource::instance().thread_initialize();
// Try to avoid deadlock in bthread
/*
if (lock_status == EBUSY || lock_status == EAGAIN) {
while (pthread_mutex_lock(&bthread::g_task_control_mutex) != 0) {
}
}
pthread_mutex_unlock(&g_worker_start_fn_mutex);
*/
}
static void g_change_server_port() {
......@@ -126,7 +130,7 @@ int main(int argc, char** argv) {
return 0;
}
//google::ParseCommandLineFlags(&argc, &argv, true);
// google::ParseCommandLineFlags(&argc, &argv, true);
g_change_server_port();
......@@ -202,7 +206,7 @@ int main(int argc, char** argv) {
}
VLOG(2) << "Succ call pthread worker start function";
//this is not used by any code segment,which can be cancelled.
// this is not used by any code segment,which can be cancelled.
if (Resource::instance().general_model_initialize(FLAGS_resource_path,
FLAGS_resource_file) != 0) {
LOG(ERROR) << "Failed to initialize general model conf: "
......
paddle_inference/paddle/include/paddle_engine.h
浏览文件 @ fe0ab9c8
......@@ -19,6 +19,7 @@
#include <map>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "core/configure/include/configure_parser.h"
#include "core/configure/inferencer_configure.pb.h"
......@@ -96,7 +97,7 @@ class EngineCore {
return true;
}
virtual int create(const configure::EngineDesc& conf) = 0;
virtual int create(const configure::EngineDesc& conf, int gpu_id) = 0;
virtual int clone(void* predictor) {
if (predictor == NULL) {
......@@ -121,7 +122,7 @@ class EngineCore {
// Paddle Inference Engine
class PaddleInferenceEngine : public EngineCore {
public:
int create(const configure::EngineDesc& engine_conf) {
int create(const configure::EngineDesc& engine_conf, int gpu_id) {
std::string model_path = engine_conf.model_dir();
if (access(model_path.c_str(), F_OK) == -1) {
LOG(ERROR) << "create paddle predictor failed, path not exits: "
......@@ -162,7 +163,11 @@ class PaddleInferenceEngine : public EngineCore {
config.SetCpuMathLibraryNumThreads(1);
if (engine_conf.has_use_gpu() && engine_conf.use_gpu()) {
// 2000MB GPU memory
config.EnableUseGpu(2000, FLAGS_gpuid);
config.EnableUseGpu(50, gpu_id);
if (engine_conf.has_gpu_multi_stream() &&
engine_conf.gpu_multi_stream()) {
config.EnableGpuMultiStream();
}
}
precision_type = GetPrecision(FLAGS_precision);
......@@ -174,8 +179,13 @@ class PaddleInferenceEngine : public EngineCore {
}
if (engine_conf.has_use_trt() && engine_conf.use_trt()) {
config.SwitchIrOptim(true);
if (!engine_conf.has_use_gpu() || !engine_conf.use_gpu()) {
config.EnableUseGpu(2000, FLAGS_gpuid);
config.EnableUseGpu(50, gpu_id);
if (engine_conf.has_gpu_multi_stream() &&
engine_conf.gpu_multi_stream()) {
config.EnableGpuMultiStream();
}
}
config.EnableTensorRtEngine(1 << 20,
max_batch,
......@@ -203,7 +213,7 @@ class PaddleInferenceEngine : public EngineCore {
if (precision_type == PrecisionType::kInt8) {
config.EnableMkldnnQuantizer();
auto quantizer_config = config.mkldnn_quantizer_config();
// TODO: warmup data
// TODO(somebody): warmup data
// quantizer_config -> SetWarmupData();
// quantizer_config -> SetWarmupBatchSize();
// quantizer_config -> SetEnabledOpTypes(4);
......
python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README.md
浏览文件 @ fe0ab9c8
......@@ -13,7 +13,8 @@ tar xf faster_rcnn_hrnetv2p_w18_1x.tar
python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
```
This model support TensorRT, if you want a faster inference, please use `--use_trt`.
This model support TensorRT, if you want a faster inference, please use `--use_trt`. But you need to do some extra work.
Please reference to https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/c%2B%2B/paddle-trt/trt_dynamic_shape_test.cc#L40
### Prediction
......
python/examples/detection/faster_rcnn_hrnetv2p_w18_1x/README_CN.md
浏览文件 @ fe0ab9c8
......@@ -13,7 +13,8 @@ wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/
tar xf faster_rcnn_hrnetv2p_w18_1x.tar
python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
```
该模型支持TensorRT，如果想要更快的预测速度，可以开启`--use_trt`选项。
该模型支持TensorRT，如果想要更快的预测速度，可以开启`--use_trt`选项,但此时需要额外设置子图的TRT变长最大最小最优shape.
请参考https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/c%2B%2B/paddle-trt/trt_dynamic_shape_test.cc#L40
### 执行预测
```
......
python/examples/detection/faster_rcnn_r50_fpn_1x_coco/README.md
浏览文件 @ fe0ab9c8
......@@ -13,7 +13,8 @@ tar xf faster_rcnn_r50_fpn_1x_coco.tar
python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
```
This model support TensorRT, if you want a faster inference, please use `--use_trt`.
This model support TensorRT, if you want a faster inference, please use `--use_trt`. But you need to do some extra work.
Please reference to https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/c%2B%2B/paddle-trt/trt_dynamic_shape_test.cc#L40
### Perform prediction
......
python/examples/detection/faster_rcnn_r50_fpn_1x_coco/README_CN.md
浏览文件 @ fe0ab9c8
......@@ -13,7 +13,8 @@ wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/
tar xf faster_rcnn_r50_fpn_1x_coco.tar
python -m paddle_serving_server.serve --model serving_server --port 9494 --gpu_ids 0
```
该模型支持TensorRT，如果想要更快的预测速度，可以开启`--use_trt`选项。
该模型支持TensorRT，如果想要更快的预测速度，可以开启`--use_trt`选项,但此时需要额外设置子图的TRT变长最大最小最优shape.
请参考https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/c%2B%2B/paddle-trt/trt_dynamic_shape_test.cc#L40
### 执行预测
```
......
python/examples/grpc_impl_example/yolov4/test_client.py
浏览文件 @ fe0ab9c8
......@@ -27,7 +27,7 @@ preprocess = Sequential([
postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
client = Client()
client.connect(['127.0.0.1:9393'])
client.set_rpc_timeout_ms(15000)
client.set_rpc_timeout_ms(100000)
im = preprocess(sys.argv[1])
fetch_map = client.predict(
......
python/examples/ocr/README.md
浏览文件 @ fe0ab9c8
......@@ -26,7 +26,7 @@ tar xf test_imgs.tar
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
python ocr_web_server.py cpu
#for gpu user
python -m paddle_serving_server.serve --model ocr_det_model --port 9293 --gpu_id 0
python -m paddle_serving_server.serve --model ocr_det_model --port 9293 --gpu_ids 0
python ocr_web_server.py gpu
```
......@@ -111,7 +111,7 @@ After the -- model parameter, the folder path of multiple model files is passed
#for cpu user
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293
#for gpu user
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293 --gpu_id 0
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293 --gpu_ids 0
```
### Client Prediction
......
python/examples/ocr/README_CN.md
浏览文件 @ fe0ab9c8
......@@ -25,7 +25,7 @@ tar xf test_imgs.tar
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
python ocr_web_server.py cpu
#for gpu user
python -m paddle_serving_server.serve --model ocr_det_model --port 9293 --gpu_id 0
python -m paddle_serving_server.serve --model ocr_det_model --port 9293 --gpu_ids 0
python ocr_web_server.py gpu
```
......@@ -110,7 +110,7 @@ python rec_web_client.py
#for cpu user
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293
#for gpu user
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293 --gpu_id 0
python -m paddle_serving_server.serve --model ocr_det_model ocr_rec_model --port 9293 --gpu_ids 0
```
### 启动客户端
......
python/paddle_serving_client/client.py
浏览文件 @ fe0ab9c8
......@@ -307,6 +307,9 @@ class Client(object):
if isinstance(feed, dict):
feed_batch.append(feed)
elif isinstance(feed, list):
# batch_size must be 1, cause batch is already in Tensor.
if len(feed) != 1:
raise ValueError("Feed only list = [dict]")
feed_batch = feed
else:
raise ValueError("Feed only accepts dict and list of dict")
......@@ -326,6 +329,7 @@ class Client(object):
fetch_names = []
counter = 0
# batch_size must be 1, cause batch is already in Tensor.
batch_size = len(feed_batch)
for key in fetch_list:
......
python/paddle_serving_server/serve.py
浏览文件 @ fe0ab9c8
......@@ -39,7 +39,16 @@ def serve_args():
"--port", type=int, default=9292, help="Port of the starting gpu")
parser.add_argument(
"--device", type=str, default="gpu", help="Type of device")
parser.add_argument("--gpu_ids", type=str, default="", help="gpu ids")
parser.add_argument(
"--gpu_ids", type=str, default="", nargs="+", help="gpu ids")
parser.add_argument(
"--op_num", type=int, default=0, nargs="+", help="Number of each op")
parser.add_argument(
"--op_max_batch",
type=int,
default=32,
nargs="+",
help="Max batch of each op")
parser.add_argument(
"--model", type=str, default="", nargs="+", help="Model for serving")
parser.add_argument(
......@@ -99,85 +108,20 @@ def serve_args():
type=str,
default=None,
help="container_id for authentication")
parser.add_argument(
"--gpu_multi_stream",
default=False,
action="store_true",
help="Use gpu_multi_stream")
return parser.parse_args()
def start_standard_model(serving_port): # pylint: disable=doc-string-missing
args = serve_args()
thread_num = args.thread
model = args.model
port = serving_port
workdir = args.workdir
device = args.device
mem_optim = args.mem_optim_off is False
ir_optim = args.ir_optim
max_body_size = args.max_body_size
use_mkl = args.use_mkl
use_encryption_model = args.use_encryption_model
use_multilang = args.use_multilang
if model == "":
print("You must specify your serving model")
exit(-1)
def start_gpu_card_model(gpu_mode, port, args): # pylint: disable=doc-string-missing
for single_model_config in args.model:
if os.path.isdir(single_model_config):
pass
elif os.path.isfile(single_model_config):
raise ValueError("The input of --model should be a dir not file.")
import paddle_serving_server as serving
op_maker = serving.OpMaker()
op_seq_maker = serving.OpSeqMaker()
read_op = op_maker.create('general_reader')
op_seq_maker.add_op(read_op)
for idx, single_model in enumerate(model):
infer_op_name = "general_infer"
#Temporary support for OCR model,it will be completely revised later
#If you want to use this, C++ server must compile with WITH_OPENCV option.
if len(model) == 2 and idx == 0 and model[0] == 'ocr_det_model':
infer_op_name = "general_detection"
general_infer_op = op_maker.create(infer_op_name)
op_seq_maker.add_op(general_infer_op)
general_response_op = op_maker.create('general_response')
op_seq_maker.add_op(general_response_op)
server = None
if use_multilang:
server = serving.MultiLangServer()
else:
server = serving.Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(thread_num)
server.set_memory_optimize(mem_optim)
server.set_ir_optimize(ir_optim)
server.use_mkl(use_mkl)
server.set_max_body_size(max_body_size)
server.set_port(port)
server.set_precision(args.precision)
server.set_use_calib(args.use_calib)
server.use_encryption_model(use_encryption_model)
if args.product_name != None:
server.set_product_name(args.product_name)
if args.container_id != None:
server.set_container_id(args.container_id)
server.load_model_config(model)
server.prepare_server(workdir=workdir, port=port, device=device)
server.run_server()
def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-string-missing
workdir = args.workdir
gpuid = int(gpuid)
device = "gpu"
if gpuid == -1:
if gpu_mode == False:
device = "cpu"
elif gpuid >= 0:
port = port + index
thread_num = args.thread
model = args.model
mem_optim = args.mem_optim_off is False
......@@ -185,8 +129,7 @@ def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-strin
use_mkl = args.use_mkl
max_body_size = args.max_body_size
use_multilang = args.use_multilang
if gpuid >= 0:
workdir = "{}_{}".format(args.workdir, gpuid)
workdir = "{}_{}".format(args.workdir, port)
if model == "":
print("You must specify your serving model")
......@@ -204,7 +147,11 @@ def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-strin
op_seq_maker.add_op(read_op)
for idx, single_model in enumerate(model):
infer_op_name = "general_infer"
if len(model) == 2 and idx == 0:
# 目前由于ocr的节点Det模型依赖于opencv的第三方库
# 只有使用ocr的时候，才会加入opencv的第三方库并编译GeneralDetectionOp
# 故此处做特殊处理，当不满足下述情况时，所添加的op默认为GeneralInferOp
# 以后可能考虑不用python脚本来生成配置
if len(model) == 2 and idx == 0 and single_model == "ocr_det_model":
infer_op_name = "general_detection"
else:
infer_op_name = "general_infer"
......@@ -226,8 +173,19 @@ def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-strin
server.set_memory_optimize(mem_optim)
server.set_ir_optimize(ir_optim)
server.set_max_body_size(max_body_size)
if args.use_trt:
if args.use_trt and device == "gpu":
server.set_trt()
server.set_ir_optimize(True)
if args.gpu_multi_stream and device == "gpu":
server.set_gpu_multi_stream()
if args.op_num:
server.set_op_num(args.op_num)
if args.op_max_batch:
server.set_op_max_batch(args.op_max_batch)
if args.use_lite:
server.set_lite()
......@@ -247,48 +205,40 @@ def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-strin
port=port,
device=device,
use_encryption_model=args.use_encryption_model)
if gpuid >= 0:
server.set_gpuid(gpuid)
if gpu_mode == True:
server.set_gpuid(args.gpu_ids)
server.run_server()
def start_multi_card(args, serving_port=None): # pylint: disable=doc-string-missing
gpus = ""
gpus = []
if serving_port == None:
serving_port = args.port
if args.gpu_ids == "":
gpus = []
else:
gpus = args.gpu_ids.split(",")
#check the gpu_id is valid or not.
gpus = args.gpu_ids
if isinstance(gpus, str):
gpus = [gpus]
if "CUDA_VISIBLE_DEVICES" in os.environ:
env_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
for ids in gpus:
if ids not in env_gpus:
print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
exit(-1)
else:
env_gpus = []
for op_gpus_str in gpus:
op_gpu_list = op_gpus_str.split(",")
for ids in op_gpu_list:
if ids not in env_gpus:
print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
exit(-1)
if args.use_lite:
print("run using paddle-lite.")
start_gpu_card_model(-1, -1, serving_port, args)
start_gpu_card_model(False, serving_port, args)
elif len(gpus) <= 0:
print("gpu_ids not set, going to run cpu service.")
start_gpu_card_model(-1, -1, serving_port, args)
start_gpu_card_model(False, serving_port, args)
else:
gpu_processes = []
for i, gpu_id in enumerate(gpus):
p = Process(
target=start_gpu_card_model,
args=(
i,
gpu_id,
serving_port,
args, ))
gpu_processes.append(p)
for p in gpu_processes:
p.start()
for p in gpu_processes:
p.join()
start_gpu_card_model(True, serving_port, args)
class MainService(BaseHTTPRequestHandler):
......@@ -395,14 +345,28 @@ if __name__ == "__main__":
from .web_service import WebService
web_service = WebService(name=args.name)
web_service.load_model_config(args.model)
gpu_ids = args.gpu_ids
if gpu_ids == "":
if args.gpu_ids == "":
gpus = []
else:
#check the gpu_id is valid or not.
gpus = args.gpu_ids
if isinstance(gpus, str):
gpus = [gpus]
if "CUDA_VISIBLE_DEVICES" in os.environ:
gpu_ids = os.environ["CUDA_VISIBLE_DEVICES"]
if len(gpu_ids) > 0:
web_service.set_gpus(gpu_ids)
env_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
for op_gpus_str in gpus:
op_gpu_list = op_gpus_str.split(",")
for ids in op_gpu_list:
if ids not in env_gpus:
print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
exit(-1)
if len(gpus) > 0:
web_service.set_gpus(gpus)
workdir = "{}_{}".format(args.workdir, args.port)
web_service.prepare_server(
workdir=args.workdir,
workdir=workdir,
port=args.port,
device=args.device,
use_lite=args.use_lite,
......@@ -410,7 +374,11 @@ if __name__ == "__main__":
ir_optim=args.ir_optim,
thread_num=args.thread,
precision=args.precision,
use_calib=args.use_calib)
use_calib=args.use_calib,
use_trt=args.use_trt,
gpu_multi_stream=args.gpu_multi_stream,
op_num=args.op_num,
op_max_batch=args.op_max_batch)
web_service.run_rpc_service()
app_instance = Flask(__name__)
......
python/paddle_serving_server/server.py
浏览文件 @ fe0ab9c8
......@@ -81,8 +81,11 @@ class Server(object):
self.use_local_bin = False
self.mkl_flag = False
self.device = "cpu"
self.gpuid = 0
self.gpuid = []
self.op_num = [0]
self.op_max_batch = [32]
self.use_trt = False
self.gpu_multi_stream = False
self.use_lite = False
self.use_xpu = False
self.model_config_paths = collections.OrderedDict()
......@@ -137,11 +140,13 @@ class Server(object):
def set_ir_optimize(self, flag=False):
self.ir_optimization = flag
# Multi-Server does not have this Function.
def set_product_name(self, product_name=None):
if product_name == None:
raise ValueError("product_name can't be None.")
self.product_name = product_name
# Multi-Server does not have this Function.
def set_container_id(self, container_id):
if container_id == None:
raise ValueError("container_id can't be None.")
......@@ -163,12 +168,21 @@ class Server(object):
def set_device(self, device="cpu"):
self.device = device
def set_gpuid(self, gpuid=0):
def set_gpuid(self, gpuid):
self.gpuid = gpuid
def set_op_num(self, op_num):
self.op_num = op_num
def set_op_max_batch(self, op_max_batch):
self.op_max_batch = op_max_batch
def set_trt(self):
self.use_trt = True
def set_gpu_multi_stream(self):
self.gpu_multi_stream = True
def set_lite(self):
self.use_lite = True
......@@ -178,6 +192,27 @@ class Server(object):
def _prepare_engine(self, model_config_paths, device, use_encryption_model):
if self.model_toolkit_conf == None:
self.model_toolkit_conf = []
self.device = device
if isinstance(self.gpuid, str):
self.gpuid = [self.gpuid]
if len(self.gpuid) == 0:
if self.device == "gpu" or self.use_trt:
self.gpuid.append("0")
else:
self.gpuid.append("-1")
if isinstance(self.op_num, int):
self.op_num = [self.op_num]
if len(self.op_num) == 0:
self.op_num.append(0)
if isinstance(self.op_max_batch, int):
self.op_max_batch = [self.op_max_batch]
if len(self.op_max_batch) == 0:
self.op_max_batch.append(32)
index = 0
for engine_name, model_config_path in model_config_paths.items():
engine = server_sdk.EngineDesc()
......@@ -186,19 +221,28 @@ class Server(object):
engine.reloadable_meta = model_config_path + "/fluid_time_file"
os.system("touch {}".format(engine.reloadable_meta))
engine.reloadable_type = "timestamp_ne"
engine.runtime_thread_num = 0
engine.batch_infer_size = 0
engine.enable_batch_align = 0
engine.runtime_thread_num = self.op_num[index % len(self.op_num)]
engine.batch_infer_size = self.op_max_batch[index %
len(self.op_max_batch)]
engine.enable_batch_align = 1
engine.model_dir = model_config_path
engine.enable_memory_optimization = self.memory_optimization
engine.enable_ir_optimization = self.ir_optimization
engine.use_trt = self.use_trt
engine.gpu_multi_stream = self.gpu_multi_stream
engine.use_lite = self.use_lite
engine.use_xpu = self.use_xpu
engine.use_gpu = False
if self.device == "gpu":
if self.device == "gpu" or self.use_trt:
engine.use_gpu = True
if len(self.gpuid) == 0:
raise ValueError("CPU: self.gpuid = -1, GPU: must set it ")
op_gpu_list = self.gpuid[index % len(self.gpuid)].split(",")
for ids in op_gpu_list:
engine.gpu_ids.extend([int(ids)])
if os.path.exists('{}/__params__'.format(model_config_path)):
engine.combined_model = True
else:
......@@ -208,6 +252,7 @@ class Server(object):
engine.type = "PADDLE_INFER"
self.model_toolkit_conf.append(server_sdk.ModelToolkitConf())
self.model_toolkit_conf[-1].engines.extend([engine])
index = index + 1
def _prepare_infer_service(self, port):
if self.infer_service_conf == None:
......@@ -332,7 +377,11 @@ class Server(object):
self.mkl_flag = flag
def check_avx(self):
p = subprocess.Popen(['cat /proc/cpuinfo | grep avx 2>/dev/null'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
p = subprocess.Popen(
['cat /proc/cpuinfo | grep avx 2>/dev/null'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
out, err = p.communicate()
if err == b'' and len(out) > 0:
return True
......@@ -431,6 +480,7 @@ class Server(object):
device="cpu",
use_encryption_model=False,
cube_conf=None):
self.device = device
if workdir == None:
workdir = "./tmp"
os.system("mkdir -p {}".format(workdir))
......@@ -533,7 +583,6 @@ class Server(object):
"-workflow_path {} " \
"-workflow_file {} " \
"-bthread_concurrency {} " \
"-gpuid {} " \
"-max_body_size {} ".format(
self.bin_path,
self.workdir,
......@@ -549,7 +598,6 @@ class Server(object):
self.workdir,
self.workflow_fn,
self.num_threads,
self.gpuid,
self.max_body_size)
print("Going to Run Comand")
print(command)
......@@ -615,9 +663,27 @@ class MultiLangServer(object):
def set_ir_optimize(self, flag=False):
self.bserver_.set_ir_optimize(flag)
def set_gpuid(self, gpuid=0):
def set_gpuid(self, gpuid):
self.bserver_.set_gpuid(gpuid)
def set_op_num(self, op_num):
self.bserver_.set_op_num(op_num)
def set_op_max_batch(self, op_max_batch):
self.bserver_.set_op_max_batch(op_max_batch)
def set_trt(self):
self.bserver_.set_trt()
def set_gpu_multi_stream(self):
self.bserver_.set_gpu_multi_stream()
def set_lite(self):
self.bserver_.set_lite()
def set_xpu(self):
self.bserver_.set_xpu()
def load_model_config(self,
server_config_dir_paths,
client_config_path=None):
......@@ -674,6 +740,7 @@ class MultiLangServer(object):
device="cpu",
use_encryption_model=False,
cube_conf=None):
self.device = device
if not self._port_is_available(port):
raise SystemExit("Port {} is already used".format(port))
default_port = 12000
......
python/paddle_serving_server/web_service.py
浏览文件 @ fe0ab9c8
......@@ -105,25 +105,33 @@ class WebService(object):
def set_gpus(self, gpus):
print("This API will be deprecated later. Please do not use it")
self.gpus = [int(x) for x in gpus.split(",")]
self.gpus = gpus
def default_rpc_service(self,
workdir="conf",
workdir,
port=9292,
gpuid=0,
gpus=-1,
thread_num=2,
mem_optim=True,
use_lite=False,
use_xpu=False,
ir_optim=False,
precision="fp32",
use_calib=False):
use_calib=False,
use_trt=False,
gpu_multi_stream=False,
op_num=None,
op_max_batch=None):
device = "gpu"
if gpuid == -1:
server = Server()
if gpus == -1:
if use_lite:
device = "arm"
else:
device = "cpu"
else:
server.set_gpuid(gpus)
op_maker = OpMaker()
op_seq_maker = OpSeqMaker()
......@@ -142,7 +150,6 @@ class WebService(object):
general_response_op = op_maker.create('general_response')
op_seq_maker.add_op(general_response_op)
server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(thread_num)
server.set_memory_optimize(mem_optim)
......@@ -151,6 +158,19 @@ class WebService(object):
server.set_precision(precision)
server.set_use_calib(use_calib)
if use_trt and device == "gpu":
server.set_trt()
server.set_ir_optimize(True)
if gpu_multi_stream and device == "gpu":
server.set_gpu_multi_stream()
if op_num:
server.set_op_num(op_num)
if op_max_batch:
server.set_op_max_batch(op_max_batch)
if use_lite:
server.set_lite()
if use_xpu:
......@@ -158,8 +178,7 @@ class WebService(object):
server.load_model_config(self.server_config_dir_paths
) #brpc Server support server_config_dir_paths
if gpuid >= 0:
server.set_gpuid(gpuid)
server.prepare_server(workdir=workdir, port=port, device=device)
return server
......@@ -180,24 +199,29 @@ class WebService(object):
use_xpu=self.use_xpu,
ir_optim=self.ir_optim,
precision=self.precision,
use_calib=self.use_calib))
use_calib=self.use_calib,
op_num=self.op_num,
op_max_batch=self.op_max_batch))
else:
for i, gpuid in enumerate(self.gpus):
self.rpc_service_list.append(
self.default_rpc_service(
"{}_{}".format(self.workdir, i),
self.port_list[i],
gpuid,
thread_num=self.thread_num,
mem_optim=self.mem_optim,
use_lite=self.use_lite,
use_xpu=self.use_xpu,
ir_optim=self.ir_optim,
precision=self.precision,
use_calib=self.use_calib))
self.rpc_service_list.append(
self.default_rpc_service(
self.workdir,
self.port_list[0],
self.gpus,
thread_num=self.thread_num,
mem_optim=self.mem_optim,
use_lite=self.use_lite,
use_xpu=self.use_xpu,
ir_optim=self.ir_optim,
precision=self.precision,
use_calib=self.use_calib,
use_trt=self.use_trt,
gpu_multi_stream=self.gpu_multi_stream,
op_num=self.op_num,
op_max_batch=self.op_max_batch))
def prepare_server(self,
workdir="",
workdir,
port=9393,
device="gpu",
precision="fp32",
......@@ -205,9 +229,12 @@ class WebService(object):
use_lite=False,
use_xpu=False,
ir_optim=False,
gpuid=0,
thread_num=2,
mem_optim=True):
mem_optim=True,
use_trt=False,
gpu_multi_stream=False,
op_num=None,
op_max_batch=None):
print("This API will be deprecated later. Please do not use it")
self.workdir = workdir
self.port = port
......@@ -219,25 +246,23 @@ class WebService(object):
self.use_xpu = use_xpu
self.ir_optim = ir_optim
self.mem_optim = mem_optim
self.gpuid = gpuid
self.port_list = []
self.use_trt = use_trt
self.gpu_multi_stream = gpu_multi_stream
self.op_num = op_num
self.op_max_batch = op_max_batch
default_port = 12000
for i in range(1000):
if port_is_available(default_port + i):
self.port_list.append(default_port + i)
if len(self.port_list) > len(self.gpus):
break
def _launch_web_service(self):
gpu_num = len(self.gpus)
self.client = Client()
self.client.load_client_config(self.client_config_path)
endpoints = ""
if gpu_num > 0:
for i in range(gpu_num):
endpoints += "127.0.0.1:{},".format(self.port_list[i])
else:
endpoints = "127.0.0.1:{}".format(self.port_list[0])
endpoints = "127.0.0.1:{}".format(self.port_list[0])
self.client.connect([endpoints])
def get_prediction(self, request):
......@@ -324,10 +349,10 @@ class WebService(object):
# default self.gpus = [0].
if len(self.gpus) == 0:
self.gpus.append(0)
gpu_id = (self.gpus[0].split(","))[0]
self.client.load_model_config(
self.server_config_dir_paths[0],
use_gpu=True,
gpu_id=self.gpus[0])
self.server_config_dir_paths[0], use_gpu=True, gpu_id=gpu_id)
else:
self.client.load_model_config(
self.server_config_dir_paths[0], use_gpu=False)
......
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